%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

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@techreport{cryptonets-applying-neural-networks-to-encrypted-data-with-high-throughput-and-accuracy,
author = {Dowlin, Nathan and Gilad-Bachrach, Ran and Laine, Kim and Lauter, Kristin and Naehrig, Michael and Wernsing, John},
title = {CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy},
booktitle = {},
year = {2016},
month = {February},
abstract = {

Applying machine learning to a problem which involves medical, financial, or other types of sensitive data, not only requires accurate predictions but also careful attention to maintaining data privacy and security. Legal and ethical requirements may prevent the use of cloud-based machine learning solutions for such tasks. In this work, we will present a method to convert learned neural networks to CryptoNets, neural networks that can be applied to encrypted data. This allows a data owner to send their data in an encrypted form to a cloud service that hosts the network. The encryption ensures that the data remains confidential since the cloud does not have access to the keys needed to decrypt it. Nevertheless, we will show that the cloud service is capable of applying the neural network to the encrypted data to make encrypted predictions, and also return them in encrypted form. These encrypted predictions can be sent back to the owner of the secret key who can decrypt them. Therefore, the cloud service does not gain any information about the raw data nor about the prediction it made.

We demonstrate CryptoNets on the MNIST optical character recognition tasks. CryptoNets achieve 99% accuracy and can make more than 51000 predictions per hour on a single PC. Therefore, they allow high throughput, accurate, and private predictions.


},
publisher = {Microsoft Research},
url = {https://www.microsoft.com/en-us/research/publication/cryptonets-applying-neural-networks-to-encrypted-data-with-high-throughput-and-accuracy/},
address = {},
pages = {},
journal = {},
volume = {},
chapter = {},
isbn = {},
}
@inproceedings{Chaudhuri:2008:PLR:2981780.2981817,
 author = {Chaudhuri, Kamalika and Monteleoni, Claire},
 title = {Privacy-preserving Logistic Regression},
 booktitle = {Proceedings of the 21st International Conference on Neural Information Processing Systems},
 series = {NIPS'08},
 year = {2008},
 isbn = {978-1-6056-0-949-2},
 location = {Vancouver, British Columbia, Canada},
 pages = {289--296},
 numpages = {8},
 url = {http://dl.acm.org/citation.cfm?id=2981780.2981817},
 acmid = {2981817},
 publisher = {Curran Associates Inc.},
 address = {USA},
}
@InProceedings{10.1007/11681878_14,
author="Dwork, Cynthia
and McSherry, Frank
and Nissim, Kobbi
and Smith, Adam",
editor="Halevi, Shai
and Rabin, Tal",
title="Calibrating Noise to Sensitivity in Private Data Analysis",
booktitle="Theory of Cryptography",
year="2006",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="265--284",
abstract="We continue a line of research initiated in [10,11]on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user.",
isbn="978-3-540-32732-5"
}
@online{homomorphicencryptionstd,
  title = {},
  author = {},
  note = {\url{http://homomorphicencryption.org/}},
  urldate = {2018-04-03}
}
@online{AIPrivacy,
  title = {Artificial Intelligence and Privacy},
  author = {Norwegian Data Protection Authority},
  note = {\url{https://www.datatilsynet.no/globalassets/global/english/ai-and-privacy.pdf}},
  urldate = {2018-04-03}
}
@online{SEALManual,
  title = {Simple Encrypted Arithmetic Library 2.3.1},
  author = {Kim Laine},
  note = {\url{https://www.microsoft.com/en-us/research/uploads/prod/2017/11/sealmanual-2-3-1.pdf}},
  urldate = {2018-09-04}
}
@online{MachineLearning,
  title = {},
  author = {},
  note = {\url{https://iq.intel.com/artificial-intelligence-and-machine-learning/}},
  urldate = {2018-04-03}
}
@online{GDPR,
  title = {{GDPR text:}},
  author = {},
  note = {\url{http://eur-lex.europa.eu/legal- content/EN/TXT/PDF/?uri=OJ:L:2016:119:FULL}},
  urldate = {2018-04-03}
}

@inproceedings{DBLP:conf/ssci/Kuri0OOAP0M17,
	Author = {Shohei Kuri and Takuya Hayashi and Toshiaki Omori and Seiichi Ozawa and Yoshinori Aono and Le Trieu Phong and Lihua Wang and Shiho Moriai},
	Bibsource = {dblp computer science bibliography, https://dblp.org},
	Biburl = {https://dblp.org/rec/bib/conf/ssci/Kuri0OOAP0M17},
	Booktitle = {2017 {IEEE} Symposium Series on Computational Intelligence, {SSCI} 2017, Honolulu, HI, USA, November 27 - Dec. 1, 2017},
	Crossref = {DBLP:conf/ssci/2017},
	Doi = {10.1109/SSCI.2017.8285190},
	Pages = {1--8},
	Timestamp = {Thu, 15 Feb 2018 11:04:22 +0100},
	Title = {Privacy preserving extreme learning machine using additively homomorphic encryption},
	Url = {https://doi.org/10.1109/SSCI.2017.8285190},
	Year = {2017},
	Bdsk-Url-1 = {https://doi.org/10.1109/SSCI.2017.8285190}}

@proceedings{DBLP:conf/ssci/2017,
	Bibsource = {dblp computer science bibliography, https://dblp.org},
	Biburl = {https://dblp.org/rec/bib/conf/ssci/2017},
	Isbn = {978-1-5386-2726-6},
	Publisher = {{IEEE}},
	Timestamp = {Thu, 08 Feb 2018 12:57:53 +0100},
	Title = {2017 {IEEE} Symposium Series on Computational Intelligence, {SSCI} 2017, Honolulu, HI, USA, November 27 - Dec. 1, 2017},
	Url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8267146},
	Year = {2017},
	Bdsk-Url-1 = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8267146}}

@article{DBLP:journals/iacr/AonoHPW15,
	Author = {Yoshinori Aono and Takuya Hayashi and Le Trieu Phong and Lihua Wang},
	Bibsource = {dblp computer science bibliography, https://dblp.org},
	Biburl = {https://dblp.org/rec/bib/journals/iacr/AonoHPW15},
	Journal = {{IACR} Cryptology ePrint Archive},
	Pages = {692},
	Timestamp = {Thu, 16 Nov 2017 16:03:55 +0100},
	Title = {Fast and Secure Linear Regression and Biometric Authentication with Security Update},
	Url = {http://eprint.iacr.org/2015/692},
	Volume = {2015},
	Year = {2015},
	Bdsk-Url-1 = {http://eprint.iacr.org/2015/692}}

  @inproceedings{inverse,
  	Author = {C. R. Rao, S. K. Mitra},
  	Booktitle = {New York:Wiley},
  	Timestamp = {Thu, 15 Feb 2018 11:04:22 +0100},
  	Title = {Generalized Inverse of Matrices and Its Applications},
  	Year = {1971}}

    @inproceedings{DBLP:conf/codaspy/AonoHPW16,
    author    = {Yoshinori Aono and
                 Takuya Hayashi and
                 Le Trieu Phong and
                 Lihua Wang},
    title     = {Scalable and Secure Logistic Regression via Homomorphic Encryption},
    booktitle = {Proceedings of the Sixth {ACM} on Conference on Data and Application
                 Security and Privacy, {CODASPY} 2016, New Orleans, LA, USA, March
                 9-11, 2016},
    pages     = {142--144},
    year      = {2016},
    crossref  = {DBLP:conf/codaspy/2016},
    url       = {http://doi.acm.org/10.1145/2857705.2857731},
    doi       = {10.1145/2857705.2857731},
    timestamp = {Thu, 16 Nov 2017 16:03:55 +0100},
    biburl    = {https://dblp.org/rec/bib/conf/codaspy/AonoHPW16},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  @proceedings{DBLP:conf/codaspy/2016,
    editor    = {Elisa Bertino and
                 Ravi Sandhu and
                 Alexander Pretschner},
    title     = {Proceedings of the Sixth {ACM} on Conference on Data and Application
                 Security and Privacy, {CODASPY} 2016, New Orleans, LA, USA, March
                 9-11, 2016},
    publisher = {{ACM}},
    year      = {2016},
    url       = {http://dl.acm.org/citation.cfm?id=2857705},
    isbn      = {978-1-4503-3935-3},
    timestamp = {Tue, 15 Mar 2016 09:10:11 +0100},
    biburl    = {https://dblp.org/rec/bib/conf/codaspy/2016},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  @inproceedings{DBLP:conf/ima/BosLLN13,
  author    = {Joppe W. Bos and
               Kristin E. Lauter and
               Jake Loftus and
               Michael Naehrig},
  title     = {Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme},
  booktitle = {Cryptography and Coding - 14th {IMA} International Conference, {IMACC}
               2013, Oxford, UK, December 17-19, 2013. Proceedings},
  pages     = {45--64},
  year      = {2013},
  crossref  = {DBLP:conf/ima/2013},
  url       = {https://doi.org/10.1007/978-3-642-45239-0_4},
  doi       = {10.1007/978-3-642-45239-0_4},
  timestamp = {Fri, 26 May 2017 00:49:09 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ima/BosLLN13},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@proceedings{DBLP:conf/ima/2013,
  editor    = {Martijn Stam},
  title     = {Cryptography and Coding - 14th {IMA} International Conference, {IMACC}
               2013, Oxford, UK, December 17-19, 2013. Proceedings},
  series    = {Lecture Notes in Computer Science},
  volume    = {8308},
  publisher = {Springer},
  year      = {2013},
  url       = {https://doi.org/10.1007/978-3-642-45239-0},
  doi       = {10.1007/978-3-642-45239-0},
  isbn      = {978-3-642-45238-3},
  timestamp = {Fri, 26 May 2017 00:49:09 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ima/2013},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/iacr/PhongAHWM17,
  author    = {Le Trieu Phong and
               Yoshinori Aono and
               Takuya Hayashi and
               Lihua Wang and
               Shiho Moriai},
  title     = {Privacy-Preserving Deep Learning via Additively Homomorphic Encryption},
  journal   = {{IACR} Cryptology ePrint Archive},
  volume    = {2017},
  pages     = {715},
  year      = {2017},
  url       = {http://eprint.iacr.org/2017/715},
  timestamp = {Thu, 16 Nov 2017 16:03:55 +0100},
  biburl    = {https://dblp.org/rec/bib/journals/iacr/PhongAHWM17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{DBLP:conf/ccs/ShokriS15,
  author    = {Reza Shokri and
               Vitaly Shmatikov},
  title     = {Privacy-Preserving Deep Learning},
  booktitle = {Proceedings of the 22nd {ACM} {SIGSAC} Conference on Computer and
               Communications Security, Denver, CO, USA, October 12-6, 2015},
  pages     = {1310--1321},
  year      = {2015},
  crossref  = {DBLP:conf/ccs/2015},
  url       = {http://doi.acm.org/10.1145/2810103.2813687},
  doi       = {10.1145/2810103.2813687},
  timestamp = {Sat, 03 Oct 2015 20:14:37 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ccs/ShokriS15},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@proceedings{DBLP:conf/ccs/2015,
  editor    = {Indrajit Ray and
               Ninghui Li and
               Christopher Kruegel},
  title     = {Proceedings of the 22nd {ACM} {SIGSAC} Conference on Computer and
               Communications Security, Denver, CO, USA, October 12-6, 2015},
  publisher = {{ACM}},
  year      = {2015},
  url       = {http://dl.acm.org/citation.cfm?id=2810103},
  isbn      = {978-1-4503-3832-5},
  timestamp = {Sat, 03 Oct 2015 19:54:04 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/ccs/2015},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{DBLP:journals/dke/SametM12,
  author    = {Saeed Samet and
               Ali Miri},
  title     = {Privacy-preserving back-propagation and extreme learning machine algorithms},
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  volume    = {79-80},
  pages     = {40--61},
  year      = {2012},
  url       = {https://doi.org/10.1016/j.datak.2012.06.001},
  doi       = {10.1016/j.datak.2012.06.001},
  timestamp = {Sat, 20 May 2017 00:26:09 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/dke/SametM12},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}
@inproceedings{Gentry:2009:FHE:1536414.1536440,
 author = {Gentry, Craig},
 title = {Fully Homomorphic Encryption Using Ideal Lattices},
 booktitle = {Proceedings of the Forty-first Annual ACM Symposium on Theory of Computing},
 series = {STOC '09},
 year = {2009},
 isbn = {978-1-60558-506-2},
 location = {Bethesda, MD, USA},
 pages = {169--178},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/1536414.1536440},
 doi = {10.1145/1536414.1536440},
 acmid = {1536440},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {fully homomorphic encryption},
} 
@misc{cryptoeprint:2012:144,
    author = {Junfeng Fan and Frederik Vercauteren},
    title = {Somewhat Practical Fully Homomorphic Encryption},
    howpublished = {Cryptology ePrint Archive, Report 2012/144},
    year = {2012},
    note = {\url{https://eprint.iacr.org/2012/144}},
}

@inproceedings{Regev:2005:LLE:1060590.1060603,
 author = {Regev, Oded},
 title = {On Lattices, Learning with Errors, Random Linear Codes, and Cryptography},
 booktitle = {Proceedings of the Thirty-seventh Annual ACM Symposium on Theory of Computing},
 series = {STOC '05},
 year = {2005},
 isbn = {1-58113-960-8},
 location = {Baltimore, MD, USA},
 pages = {84--93},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/1060590.1060603},
 doi = {10.1145/1060590.1060603},
 acmid = {1060603},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {computational learning theory, cryptography, lattices, public key encryption, quantum computing, statistical queries},
}
@techreport{HomomorphicEncryptionSecurityStandard,
 author = {Martin Albrecht and Melissa Chase and Hao Chen and Jintai Ding and Shafi Goldwasser and Sergey Gorbunov and Jeffrey Hoffstein and Kristin Lauter and Satya Lokam and Daniele Micciancio and Dustin Moody and Travis Morrison and Amit Sahai and Vinod Vaikuntanathan},
 title = {Homomorphic Encryption Security Standard},
 institution= {HomomorphicEncryption.org},
 publisher = {HomomorphicEncryption.org},
 address = {Cambridge MA},
 year = {2018},
 month = {March}
 }
 @inproceedings{Alippi:2018:MCN:3207947.3207995,
 author = {Alippi, Cesare and Disabato, Simone and Roveri, Manuel},
 title = {Moving Convolutional Neural Networks to Embedded Systems: The Alexnet and VGG-16 Case},
 booktitle = {Proceedings of the 17th ACM/IEEE International Conference on Information Processing in Sensor Networks},
 series = {IPSN '18},
 year = {2018},
 isbn = {978-1-5386-5298-5},
 location = {Porto, Portugal},
 pages = {212--223},
 numpages = {12},
 url = {https://doi.org/10.1109/IPSN.2018.00049},
 doi = {10.1109/IPSN.2018.00049},
 acmid = {3207995},
 publisher = {IEEE Press},
 address = {Piscataway, NJ, USA},
 keywords = {approximate computing, convolutional neural networks, deep learning, embedded systems},
} 
@article{DBLP:journals/corr/abs-1205-2926,
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@article{journals/iacr/ArmknechtBCGJRS15,
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@inproceedings{Lopez-Alt:2012:OMC:2213977.2214086,
 author = {L\'{o}pez-Alt, Adriana and Tromer, Eran and Vaikuntanathan, Vinod},
 title = {On-the-fly Multiparty Computation on the Cloud via Multikey Fully Homomorphic Encryption},
 booktitle = {Proceedings of the Forty-fourth Annual ACM Symposium on Theory of Computing},
 series = {STOC '12},
 year = {2012},
 isbn = {978-1-4503-1245-5},
 location = {New York, New York, USA},
 pages = {1219--1234},
 numpages = {16},
 url = {http://doi.acm.org/10.1145/2213977.2214086},
 doi = {10.1145/2213977.2214086},
 acmid = {2214086},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {NTRU encryption, cloud computing, fully homomorphic encryption, multiparty computation},
}
 @phdthesis{homenc,
     author = {Craig Gentry},
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}
@unpublished{migliore:hal-01394362,
  TITLE = {{Determination and exploration of practical parameters for the latest Somewhat Homomorphic Encryption (SHE) Schemes}},
  AUTHOR = {Migliore, Vincent and Bonnoron, Guillaume and Fontaine, Caroline},
  URL = {https://hal.archives-ouvertes.fr/hal-01394362},
  NOTE = {working paper or preprint},
  YEAR = {2016},
  MONTH = Oct,
  KEYWORDS = {SHE ;  parameter ;  homomorphic},
  PDF = {https://hal.archives-ouvertes.fr/hal-01394362/file/determination-exploration-practical.pdf},
  HAL_ID = {hal-01394362},
  HAL_VERSION = {v3},
}
@inproceedings{crypto-2013-24633,
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  doi={10.1007/978-3-642-40041-4_5},
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@article{Bioinformatics,
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               Michael Naehrig and
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 keywords = {bootstrapping, fully homomorphic encryption, learning with errors, modulus reduction},
} 
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}
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title = {Secure Kalman Filter State Estimation by Partially Homomorphic Encryption},
year = {2018},
volume = {00},
number = {},
pages = {345-346},
keywords={Encryption;State estimation;Kalman filters;Sensors;Smart grids},
doi = {10.1109/ICCPS.2018.00046},
url = {doi.ieeecomputersociety.org/10.1109/ICCPS.2018.00046},
ISSN = {},
month={Apr}
}
@InProceedings{Paillier,
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year="1999",
publisher="Springer Berlin Heidelberg",
address="Berlin, Heidelberg",
pages="223--238",
abstract="This paper investigates a novel computational problem, namely the Composite Residuosity Class Problem, and its applications to public-key cryptography. We propose a new trapdoor mechanism and derive from this technique three encryption schemes: a trapdoor permutation and two homomorphic probabilistic encryption schemes computationally comparable to RSA. Our cryptosystems, based on usual modular arithmetics, are provably secure under appropriate assumptions in the standard model.",
isbn="978-3-540-48910-8"
}
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@proceedings{DBLP:conf/pkc/2010,
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  series    = {Lecture Notes in Computer Science},
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@inproceedings{DBLP:conf/eurocrypt/DijkGHV10,
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@proceedings{DBLP:conf/eurocrypt/2010,
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@book{gamma1994design,
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  interhash = {7fe32957be97afaf4ecb38b5490d23b4},
  intrahash = {7e3f1154ab1fbce54752a46dba7f2217},
  isbn = {0201633612},
  keywords = {DBIS Design Object-Oriented Patterns SS2010 Seminar Software},
  publisher = {Addison-Wesley Professional},
  timestamp = {2010-06-05T16:40:25.000+0200},
  title = {Design Patterns: Elements of Reusable Object-Oriented Software},
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@inproceedings{pytorch,
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@Inbook{Biryukov2011,
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@inproceedings{DBLP:conf/stoc/GoldwasserM82,
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@proceedings{DBLP:conf/stoc/STOC14,
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@incollection{DBLP:reference/crypt/Kaliski05xd,
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@book{DBLP:reference/crypt/2005,
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@inproceedings{mccurleydiscrete,
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@incollection{jager2012generic,
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@inproceedings{DBLP:conf/tcc/BonehGN05,
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@proceedings{DBLP:conf/tcc/2005,
  editor    = {Joe Kilian},
  title     = {Theory of Cryptography, Second Theory of Cryptography Conference,
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  series    = {Lecture Notes in Computer Science},
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  publisher = {Springer},
  year      = {2005},
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@article{gjosteen2004subgroup,
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@inproceedings{Krizhevsky:2012:ICD:2999134.2999257,
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 title = {ImageNet Classification with Deep Convolutional Neural Networks},
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 series = {NIPS'12},
 year = {2012},
 location = {Lake Tahoe, Nevada},
 pages = {1097--1105},
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 url = {http://dl.acm.org/citation.cfm?id=2999134.2999257},
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 publisher = {Curran Associates Inc.},
 address = {USA},
} 
@inproceedings{BN,
title = {Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift},
author  = {Sergey Ioffe and Christian Szegedy},
year  = {2015},
URL = {http://jmlr.org/proceedings/papers/v37/ioffe15.pdf},
pages = {448-456}
}
@article{DBLP:journals/corr/XieBFGLN14,
  author    = {Pengtao Xie and
               Misha Bilenko and
               Tom Finley and
               Ran Gilad{-}Bachrach and
               Kristin E. Lauter and
               Michael Naehrig},
  title     = {Crypto-Nets: Neural Networks over Encrypted Data},
  journal   = {CoRR},
  volume    = {abs/1412.6181},
  year      = {2014}
}
@article{DBLP:journals/corr/SimonyanZ14a,
  author    = {Karen Simonyan and
               Andrew Zisserman},
  title     = {Very Deep Convolutional Networks for Large-Scale Image Recognition},
  journal   = {CoRR},
  volume    = {abs/1409.1556},
  year      = {2014}
}
@article{DBLP:journals/corr/SermanetEZMFL13,
  author    = {Pierre Sermanet and
               David Eigen and
               Xiang Zhang and
               Micha{\"{e}}l Mathieu and
               Rob Fergus and
               Yann LeCun},
  title     = {OverFeat: Integrated Recognition, Localization and Detection using
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  journal   = {CoRR},
  volume    = {abs/1312.6229},
  year      = {2013}
}
@inproceedings{43022,
title = {Going Deeper with Convolutions},
author  = {Christian Szegedy and Wei Liu and Yangqing Jia and Pierre Sermanet and Scott Reed and Dragomir Anguelov and Dumitru Erhan and Vincent Vanhoucke and Andrew Rabinovich},
year  = {2015},
URL = {http://arxiv.org/abs/1409.4842},
booktitle = {Computer Vision and Pattern Recognition (CVPR)}
}
@article{DBLP:journals/corr/HeZRS15,
  author    = {Kaiming He and
               Xiangyu Zhang and
               Shaoqing Ren and
               Jian Sun},
  title     = {Deep Residual Learning for Image Recognition},
  journal   = {CoRR},
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  year      = {2015}
}
@online{CrCNN,
  title = {Crypto Convolutional Neural Network library},
  author = {Carmen Barletta},
  note = {\url{https://github.com/barlettacarmen/CrCNN}},
  urldate = {2018-12-02}
}

